Modeling the relationship between cervical cancer mortality and trace elements based on genetic algorithm-partial least squares and support vector machines

The relationship between the mortality of cervical cancer and soil trace elements of 23 regions of China was investigated. A total of 25 elements (i.e., Na, K, Mg, Ca, Sr, Hg, Pb, B, Tm, Th, U, Sn, Hf, Bi, Ta, Te, Mo, Br, I, As, Cr, Cu, Fe, Zn, and Se) were considered. First, 23 samples were split into the training set with 12 samples and the test set with 11 samples. Then, a combination strategy called genetic algorithm-partial least squares (GA-PLS) was used to pick out five important elements. i.e., Br, Ta, Pb, Cr, and As. Afterwards, the classic partial least squares (PLS) model and least square support vector machine (LSSVM) model were developed and compared. The results revealed that the SVM model significantly outperforms the PLS model, indicating that the combination of GA-PLS and LSSVM can serve as a potential tool for predicting the mortality of cancer based on trace elements.